Bilal E.,IBM |
Dutkowski J.,University of California at San Diego |
Guinney J.,Sage Bionetworks |
Jang I.S.,Sage Bionetworks |
And 28 more authors.
PLoS Computational Biology | Year: 2013
Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models. © 2013 Bilal et al. Source
Silwal-Pandit L.,University of Oslo |
Silwal-Pandit L.,Institute for Clinical Medicine |
Vollan H.K.M.,University of Oslo |
Vollan H.K.M.,Institute for Clinical Medicine |
And 23 more authors.
Clinical Cancer Research | Year: 2014
Purpose: In breast cancer, the TP53 gene is frequently mutated and the mutations have been associated with poor prognosis. The prognostic impact of the different types of TP53 mutations across the different molecular subtypes is still poorly understood. Here, we characterize the spectrum and prognostic significance of TP53 mutations with respect to the PAM50 subtypes and integrative clusters (IC). Experimental Design: TP53 mutation status was obtained for 1,420 tumor samples from the METABRIC cohort by sequencing all coding exons using the Sanger method. Results: TP53 mutations were found in 28.3% of the tumors, conferring a worse overall and breast cancer-specific survival [HR = 2.03; 95% confidence interval (CI), 1.65-2.48, P<0.001], and were also found to be an independent marker of poor prognosis in estrogen receptor-positive cases (HR = 1.86; 95% CI, 1.39-2.49, P < 0.001). The mutation spectrum of TP53 varied between the breast cancer subtypes, and individual alterations showed subtype-specific association. TP53 mutations were associated with increased mortality in patients with luminal B, HER2-enriched, and normal-like tumors, but not in patients with luminal A and basal-like tumors. Similar observations were made in ICs, where mutation associated with poorer outcome in ICI, IC4, and IC5. The combined effect of TP53 mutation, TP53 LOH, and MDM2 amplification on mortality was additive. Conclusion: This study reveals that TP53 mutations have different clinical relevance in molecular subtypes of breast cancer, and suggests diverse roles for TP53 in the biology underlying breast cancer development. © 2014 American Association for Cancer Research. Source
Vollan H.K.M.,University of Cambridge |
Vollan H.K.M.,University of Oslo |
Rueda O.M.,University of Cambridge |
Chin S.-F.,University of Cambridge |
And 91 more authors.
Molecular Oncology | Year: 2015
Complex focal chromosomal rearrangements in cancer genomes, also called "firestorms", can be scored from DNA copy number data. The complex arm-wise aberration index (CAAI) is a score that captures DNA copy number alterations that appear as focal complex events in tumors, and has potential prognostic value in breast cancer. This study aimed to validate this DNA-based prognostic index in breast cancer and test for the first time its potential prognostic value in ovarian cancer. Copy number alteration (CNA) data from 1950 breast carcinomas (METABRIC cohort) and 508 high-grade serous ovarian carcinomas (TCGA dataset) were analyzed. Cases were classified as CAAI positive if at least one complex focal event was scored. Complex alterations were frequently localized on chromosome 8p (. n=159), 17q (. n=176) and 11q (. n=251). CAAI events on 11q were most frequent in estrogen receptor positive (ER+) cases and on 17q in estrogen receptor negative (ER-) cases. We found only a modest correlation between CAAI and the overall rate of genomic instability (GII) and number of breakpoints (. r=0.27 and r=0.42, p<0.001). Breast cancer specific survival (BCSS), overall survival (OS) and ovarian cancer progression free survival (PFS) were used as clinical end points in Cox proportional hazard model survival analyses. CAAI positive breast cancers (43%) had higher mortality: hazard ratio (HR) of 1.94 (95%CI, 1.62-2.32) for BCSS, and of 1.49 (95%CI, 1.30-1.71) for OS. Representations of the 70-gene and the 21-gene predictors were compared with CAAI in multivariable models and CAAI was independently significant with a Cox adjusted HR of 1.56 (95%CI, 1.23-1.99) for ER+ and 1.55 (95%CI, 1.11-2.18) for ER- disease. None of the expression-based predictors were prognostic in the ER- subset. We found that a model including CAAI and the two expression-based prognostic signatures outperformed a model including the 21-gene and 70-gene signatures but excluding CAAI. Inclusion of CAAI in the clinical prognostication tool PREDICT significantly improved its performance. CAAI positive ovarian cancers (52%) also had worse prognosis: HRs of 1.3 (95%CI, 1.1-1.7) for PFS and 1.3 (95%CI, 1.1-1.6) for OS. This study validates CAAI as an independent predictor of survival in both ER+ and ER- breast cancer and reveals a significant prognostic value for CAAI in high-grade serous ovarian cancer. © 2014 The Authors. Source